Digital signal analysis, with hierarchical segmentation

ABSTRACT

The invention concerns a method of analyzing a set of data representing physical quantities, including the steps of: 
     decomposition (E 20 ) of the set of data on a plurality of resolution levels, 
     first segmentation (E 21 ) of at least one sub-part of a given resolution level, into at least two homogeneous regions, said given resolution level not being the highest resolution level in the decomposition, 
     characterized in that it includes the steps of: 
     extraction (E 81 ) of contour data from the result of the segmentation of the previous steps, 
     second segmentation (E 25 ) of at least one sub-part of the resolution level higher than the given level into at least one homogeneous region, as a function of the contour data extracted.

The present invention concerns in general terms the analysis of adigital signal and proposes for this purpose a device and method foranalysing a digital signal by decomposition into a plurality ofresolution levels, and segmentation.

The purpose of the analysis is to provide a hierarchical segmentation ofthe signal, thus make it possible to access the objects or regionspresent in an image, at several resolution levels, with several possiblelevels of detail. Access to the objects of an image can be used fordifferent purposes:

selective coding of the objects of the image, granting a higher codingquality to the “important” objects in the image,

progressive transmission of the date of the image, with transmission ofthe more important objects before the others,

extraction of a particular objects from the image, with a view to itsmanipulation, transmission, coding, and storage.

The present invention is more particularly applicable to the analysis ofa digital signal. Hereinafter, the concern will more particularly bewith the analysis of digital images or video sequences. A video sequenceis defined as a succession of digital images.

There exist several known ways of effecting the decomposition of asignal on several resolution levels; it is for example possible to useGaussian/Laplacian pyramids, or to decompose the signal into frequencysub-bands at several resolution levels.

The remainder of this description will be concerned with the secondcase, but it is important to note that the present invention applies toall known multi-resolution decompositions.

In the particular case of a decomposition into frequency sub-bands, thedecomposition consists of creating, from the digital signal, a set ofsub-bands each containing a limited frequency spectrum. The sub-bandscan be of difference resolutions, the resolution of a sub-band being thenumber of samples per unit length used for representing this sub-band.In the case of an image digital signal, a frequency sub-band of thissignal can be considered to be an image, that is to say a bi-dimensionalarray of digital values.

The decomposition of a signal into frequency sub-bands makes it possibleto decorrelate the signals so as to eliminate the redundancy existing inthe digital image prior to the compression proper. The sub-bands canthen be compressed more effectively than the original signal. Moreover,the low sub-band of such a decomposition is a faithful reproduction, ata lower resolution, of the original image. It is therefore particularlywell suited to segmentation.

The segmentation of a digital image will make it possible to effect apartitioning of the image into homogeneous regions which do not overlapin this context, the image is considered to consist of objects with twodimensions. The segmentation is a low-level process whose purpose is toeffect a partitioning of the image into a certain number of sub-elementscalled regions. The partitioning is such that the regions aredisconnected and their joining constitutes the image. The regionscorrespond or do not correspond to objects in the image, the termobjects referring to information of a semantic nature. Very often,however, an object corresponds to a region or set of regions Each regioncan be represented by information representing its shape, colour ortexture. The homogeneity of the region of course depends on a particularcriteria of ho homogeneity; proximity of the average values orpreservation of the contrast or colour, for ample.

Object means an entity of the image corresponding to a semantic unit,for example the face of a person. An object can consist of one or moreregions contained in the image. Hereinafter the term object or regionwill be used indifferently.

Conventionally, the segmentation of the digital image is effected on asingle resolution level, which is the resolution of the image itself.Conventionally, the segmentation methods include a first step known asmarking, that is to say the interior of the regions housing a localhomogeneity is extracted from the image Next a decision stop preciselydefines the contours of the areas containing homogeneous data. At theend of this step, each pixel of the image is associated with a labelidentifying the region to which it belongs. The set of all of the labelsof all the pixels is called a segmentation map,

This type of segmentation makes it possible to obtain a relativelyeffective segmentation of the image but has the drawbacks of being slowand not very robust and presenting all the objects at the sameresolution.

This is the case for example with the so called MPEG4 standard (from theEnglish “Motion Picture Expert Group”), for which an ISO/IEC standard iscurrently being produced, in the MPEG4 coder, and more particularly inthe case of the coding of fixed images, the decomposition of the imageinto frequency sub-bands is used conjointly with a segmentation of theimage. A step prior to coding (not standardised) is responsible forisolating the objects of the image (video objects) and representing eachof the these objects by a mask. In the case of a binary mask, thespatial support of the mask has the same size as the original image anda point on the mask at the value 1 (or respectively 0) indicates thatthe pixel at the same position in the image belongs to the object (orrespectively is outside the object).

For each object, the mask is then transmitted to a shape decoder whilstthe texture for each object is decomposed into sub-bands, and thesub-bands are then transmitted to a texture decoder.

This method has a certain number of drawbacks. This is because theobject is accessible only at its highest resolution level there is noprogressivity in segmentation. Moreover, the number of objectsmanipulated is a priori the same at all levels, whilst it may be moreadvantageous to have a number of objects increasing with the (spatial)resolution, that is to say a true conjoint scalability between theresolution and the number of objects.

The article “Multiresolution adaptative image segmentation based onglobal and local statistics” by Boukerroui, Basset and Baekurt, whichappeared in IEEE international Conference on Image Processing, 24-28Oct. 1999, vol. 1 pages 358 to 361, describes a hierarchicalsegmentation based on a multiresolution pyramid of an image, effected bydiscrete wavelet transform, known as DWT.

In addition, the article “Multiresolution image segmentation forregion-based motion estimation and compensation” by Salgado, Garcia,Menendez and Rendon, which appeared in IEEE International Conference onimage Processing, 24-28 Oct. 1999, vol. 2, pages 135 to 139, describes ahierarchical segmentation also based on a multiresolution pyramid of animage. A partitioning of the image effected at the lowest resolutionlevel is projected onto the higher resolution levels.

However, none of these known methods provides access to the region orobjects with different resolution levels, in a consistent and coherentmanner. Coherent means here that an object at a given resolution levelalways descends from a single object with a lower resolution (parent),and gives rise to at least one object at the higher resolution level(child or children).

The present invention aims to remedy the drawbacks of the prior articleby providing a method and device for the hierarchical segmentation of adigital signal which offers access to the regions or objects atdifferent resolution levels, in a consistent and coherent manner.

In this context the invention concerns a method of analysing a set ofdata representing physical quantities, including the steps of,

decomposition of the set of data on a plurality of resolution levels,

segmentation of at least a sub-part of a given resolution level, into atleast two homogeneous regions, said given resolution level not being thehighest resolution level in the decomposition.

characterised in that it includes the steps of:

storage of information representing at least part of the result of thesegmentation of the previous step,

segmentation of at least one sub-part of the higher resolution levelinto at least one homogeneous region, according to the informationstored.

More particularly, the invention proposes a method of analysing a set ofdata representing physical quantities, including the steps of:

decomposition of the set of data on a plurality of resolution levels,

first segmentation of at least one sub-part of a given resolution level,into at least two homogeneous regions, said given resolution level notbeing the highest resolution level in the decomposition,

characterised in that it includes the steps of;

extraction of contour data from the result of the segmentation of theprevious step,

second segmentation of at least one sub-part of the resolution levelhigher than the given level into at least one homogeneous region, as afunction of the contour data extracted.

Correlatively, the invention proposes a device for analysing a set ofdata representing physical quantities, having:

means of decomposing the set of data on a plurality of resolutionlevels,

means for the fir segmentation of at least one sub-part of a givenresolution level, into at least two homogeneous regions, said givenresolution level not being the highest resolution level in thedecomposition,

characterised in that it has:

means of extracting contour data from the result of the segmentation ofthe previous step,

means for the second segmentation of at least one sub-part of theresolution level higher than the given resolution level into at leastone homogeneous region, as a function of the extracted contour data.

By virtue of the invention, the segmentation is coherent, or continuous:an object with a given resolution level always descends from a singleobject with a lower resolution level (parent), and gives rise to atleast one object at the higher resolution level (a child or children).

In addition, there is a hierarchical segmentation (at several resolutionlevels). The advantages of hierarchical segmentation are many:

progressive object-based coding and transmission can be effective atseveral resolution levels,

it is possible to segment the image according to finer and finer detailsor objects on progressing through the resolution levels; the user canthus access the objects of the image with a more or less great level ofdetail. For example, at a lower resolution, the user often needs only acoarse segmentation (the chest and face of a person). whilst he wouldwish to access a higher level of detail when the resolution increases(eyes, nose, mouth on the face, etc).

the robustness of the global segmentation and the speed of segmentationare generally higher in the case of a hierarchical segmentation than inthe case of a segmentation at a single level.

According to a preferred characteristic, the decomposition is at eachresolution level a decomposition into a plurality of frequencysub-bands. This type of decomposition is normally used in imageprocessing, and is simple and rapid to implement.

According to another preferred characteristic, the first segmentation iseffected on a low-frequency sub-band of the given resolution level, Thisis because the lower-frequency sub-band forms a “simplified” version ofthe signal, and it is consequently advantageous to effect thesegmentation on this sub-band.

According to preferred characteristics, the given resolution level isthe lowest resolution level, and the extraction and second segmentationsteps are effective iteratively as far as the highest resolution level.Thus a hierarchical segmentation is obtained on all the resolutionlevels.

According to a preferred characteristic, the second segmentationincludes:

a projection of a contour image resulting from the firs segmentation, onsaid at least one sub-part of tie higher resolution level which is to besegmented,

a marking of the coefficients of said at least one sub-part of thehigher resolution level, as a function of the result of the projection,and

a decision.

According to another preferred characteristic, the second segmentationis effected on a lower-frequency sub-band of the higher resolutionlevel. As with the first segmentation, it is advantageous to effect thesecond segmentation on a “simplified” version of the signal.

According to a preferred characteristic, the contour data extractionincludes, for each coefficient of the segmented sub-part:

a comparison of said coefficient with its neighbours,

setting of a contour coefficient corresponding to said coefficient to afirst predetermined value if the coefficient is different from at leastone of its neighbours, or to a second predetermined value if thecoefficient is similar to all its neighbours.

The device has means adapted to implement the above characteristics.

The invention also concerns a digital apparatus including the analysisdevice, or means of implementing the analysis method. The advantages ofthe device and digital apparatus are identical to those disclosed above.

The invention also concerns an information storage means, which can beread by a computer or microprocessors integrated or not integrated intothe device, possibly removable, storing a program implementing theanalysis method.

The characteristics and advantages of the present invention will emergemore dearly from a reading of a preferred embodiment illustrated by theaccompanying drawings, in which:

FIG. 1 is an embodiment of a device implementing the invention.

FIG. 2 depicts an analysis algorithm according to the present invention,

FIG. 3 is an algorithm for implementing the non-assisted segmentationstep included in the analysis algorithm of FIG. 2,

FIG. 4 is an algorithm for implementing the assisted segmentation stepincluded in the analysis algorithm of FIG. 2,

FIG. 5 is an algorithm showing the assisted segmentation steps accordingto the present invention,

FIG. 6 is a block diagram of a device implementing the invention,

FIG. 7 is a circuit for decomposition into frequency sub-bands includedin the device of FIG. 6,

FIG. 8 is a digital image to be analysed according to the presentinvention,

FIG. 9 is an image decomposed into sub-bands according to the presentinvention.

According to the chosen embodiment depicted in FIG. 1, a deviseimplementing the invention is for example a microcomputer connected todifferent peripherals, for example a digital camera 107 (or a scanner,or any image acquisition or storage means) connected to a graphics cardand supplying information to be analysed according to the invention.

The device 10 has a communicating interface 112 connected to a network113 able to transmit digital data to be analysed or conversely totransmit data analysed by the device. The device 10 also has a storagemeans 108 such as for example a hard disk. It also has a drive 109 for adisk 110 This disk 110 can be a diskette, a CD-ROM or a DVD-ROM forexample. This disk 110, like the disk 108, can contain data processedaccording to the invention as well as the program or programmesimplementing the invention which, once read by the device 10, will bestored in the hard disk 108. According to a variant, the programenabling the device to implement the invention can be stored in readonly memory 102 (referred to as ROM in the drawing). In a secondvariant, the program can be received in order to be stored in anidentical fashion to that described previously by means of thecommunication network 113.

The device 10 is connected to a microphone 111. The data to be processedaccording to the invention will in this case be of the audio signal.

This same device has a screen 104 for displaying the data to be analysedor transmitted or serving as an interface with the user, who will beable to parameterise certain analysis modes, using the keyboard 114 orany other means (mouse for example).

The central unit 100 (referred to as CPU in the drawing) executes theinstructions relating to the implementation of the invention,instructions stored in the read only memory 102 or in the other storageelements. On powering up, the analysis program stored in a non-volatilememory, for example the ROM 102, are transferred into the random accessmemory RAM 103, which would then contain the executable code of theinvention as well as registers for storing the variables necessary forimplementing the invention.

In more general terms, an information storage means, which can be readby a computer or microprocessor, integrated or not into the device,possibly removable, stores a program for implementing the methodaccording to the invention

The communication bus 101 affords communication between the differentelements included in the microcomputer 10 or connected to it. Therepresentation of the bus 101 is not limitative and notable the centralunit 100 is able to communicate instructions to any element of themicrocomputer 10 directly or by means of another element of themicrocomputer 10.

With reference to FIG. 2, an embodiment of the method of analyzingaccording to the invention an image IM includes steps E20 to E27 whichare run through by the central unit of the device 10 described above.The method includes overall the decomposition of the image on aplurality of resolution levels, then the segmentation of at least asub-part of a given resolution level, into at least two homogeneousregions, the said given resolution level not being the highestresolution level in the decomposition, followed by the storage ofinformation representing at least part of the result of the segmentationof the previous step and finally the segmentation of at least a sub-partof the higher resolution level into at least one homogeneous region,according to the information stored.

More precisely, the invention includes the extraction of contour datafrom the result of the segmentation of the previous steps and thesegmentation of at least a sub-part of the higher resolution level intoat least one homogeneous region, according to the contour dataextracted.

Step E20 is the decomposition of the image IM into a plurality ofresolution levels and more particularly into a plurality of frequencysub-bands with different resolutions, as will be detailed below withreference to FIG. 9. For example, the decomposition is effected on threeresolution levels thus supplying sub-bands LL₃, HL₃ LH₃ and HH₃ with thelowest resolution RES₃, the sub-bands HL₂, LH₂ and HH₂ of theintermediate resolution level RES₂, and the sub-bands HL₁, LH₁ and HH₁of the highest resolution RES₁. It should be noted that, during thisstep, all the sub-bands LL_(n), HL_(n), LH_(n) and HH_(n) of aresolution level RES_(n), where n is an integer, can be stored, or thelower-frequency sub-band LL_(n) can be stored only for the lowestresolution level, and synthesised for the other levels.

The following step E21 consists of segmenting the low sub-band LL_(N) inorder to supply a segmentation of level N, N being an integer equal forexample to 3 if 3 decomposition levels are effected; the low sub-bandLL_(N) is the sub-band LL₃ in our example. The result of thesegmentation is a segmentation S_(N) containing at least two distinctregions covering all the segmented sub-band. This segmentation step isdetailed below with reference to FIG. 3.

During the following step E22, a parameter i is initialised to the value0. The parameter i indicates the current resolution level N−i, where Ncorresponds to the total number of decomposition levels, here 3. Thisindicator will be subsequently updated at each iteration.

Step E22 is followed by E23, during which at least one region of thesegmentation of resolution level N−i is stored in order to be usedsubsequently during the segmentation step of the immediately higherresolution level.

Step E23 is followed by E24 which, where the low sub-bands LL_(n) havenot been stored, effects a synthesis on the sub-bands of the resolutionlevel N−i in question. The result of the synthesis step is areconstructed low sub-band LLS_(N−i−1), of resolution immediately higherthan the sub-bands which were used for synthesis. Thus, from thesub-bands LL₃, LH₃, HL₃ and HH₃ a low sub-band LLS₂ of level 2 isreconstructed. Naturally, this step is replaced by a simple reading inmemory of the higher resolution level in the case where all thesub-bands have been stored during the decomposition.

During step E25, there is effected a second segmentation of at least onesub-part of the reconstructed level and more precisely of the sub-bandLLS_(N−i−1) obtained during step E24. This so-called assisted secondsegmentation uses two sources as a input: the information stored at stepE23, and the current low sub-band LLS_(N−i−1). The purpose of thisassisted segmentation step is to provide a segmentation at the currentresolution level N−i−1, coherent with the previous resolution level N−i.

Coherent means that the segmentation is continuous, that is to say aregion or even an object can be followed from one level to the other. Inparticular, this coherence implies that an object of level N−i alwaysexists at level N−i−1, and descends from at least one parent object oflevel N−i−1, if this level exists; during passage to the level N−i−1 ofhigher resolution, the object of resolution N−i can have beensub-divided into several objects, but cannot have been merged with otherobjects of resolution N−i; also it cannot have overflowed (apart from anarrow area of tolerance on the contours, as will be explained below)onto another object of resolution level N−i. The assisted segmentationwill be described in more detail below.

The following step E26 is a test in order to determine whether all thelevels of the decomposition have been processed, that is to say whetherthe parameter 1 is equal to N−1. If the test is negative, there stillremains at least one level to be processed, and in this case step E26 isfollowed by step E27, which increments the parameter i by one unit. StepE27 is followed by the previously described step E23.

If the test of step E26 is positive, analysis of the digital image isterminated.

The step E21 of segmentation of a sub-part of a resolution level andmore particularly of a sub-band is detailed in FIG. 3 and includes substeps E90 to E92.

Step E90 effects a simplification of the sub-band. A simplified versionof the sub-band, more generally of an image, will for example beobtained by applying to the latter a morphological opening/closingoperator, followed by a morphological reconstruction. A completedescription of this method can be found in the article by PhillippeSalembier entitled “Morphological multiscale segmentation for imagecoding” which appeared in the magazine “Signal Processing” N° 38 of1994. This type of processing eliminates the objects smaller than acertain size, and restores the contours of the objects which have notbeen eliminated. At the end of this step there is therefore a simplifiedversion of the sub-band which will be easier to process by means of thefollowing steps.

The following step E91 is the marking of or extraction of the markersfrom the simplified sub-band. This step identifies the presence of thehomogeneous regions of the simplified sub-band, using a criterion whichcan for example be a criterion of homogeneity of the intensity of theregion (flat region). In concrete terms, use is made here for example ofa region growth algorithm, the sub-band is scanned in its entirety (forexample from top to bottom and right to left). A “kernel” is sought,that is to say a point, here a coefficient, representing a new region(the first coefficient of the sub-band will automatically be one ofthese). The characteristic of this region (the mean value) is calculatedon the basis of this point. Then all the neighbours of this point areexamined, and for each of the neighbours two possibilities are offered:

If the point encountered has an intensity close to the mean value of theregion in question, it is allocated to the current region, and thestatistics of this region are updated according to this new element,

if the point encountered has an intensity which is different (in thesense of a proximity criterion) from the mean value of the region, it isnot allocated to the region (it may subsequently be considered to be anew “kernel” representing a new region).

All the points allocated to the current region are then themselvessubjected to examination, that is to say all their neighbours areexamined (growth phase).

The processing of the region continues thus until all the neighbouringpoints to the points belonging to the region have been examined. At theend of this processing, the region is considered good or bad if it isbad (typically too small), it is the decision step which will processthe points of the region in question. If it is good, the processing isended for it. A unique label or identifier is then allocated to all thepoints in the region. The global processing then continues with thesearch for a new kernel.

The following step E92 is the decision. It consists of attaching to aregion all the points which do not have a label at the end of themarking step E91 (typically the points which have been attached toexcessively small regions). This step can be performed simply byconsidering each of the points which do not have a label, and byallocating it to the neighbouring region to which it is closest (in thesense of a proximity criterion),

FIG. 4 depicts the step E25 of assisted segmentation of a sub-part of aresolution level and more particularly a sub-band. This step includessub-steps E80 to E84.

The method includes overall the segmentation of the current low sub-bandLLS_(N−i−1) by virtue of the extraction of the contours of thesegmentation of the lower resolution level.

Step E80 effects a simplification of the sub-band. A simplified versionof the sub-band, more generally of an image, will for example beobtained by applying to the latter a morphological opening/closingoperator, followed by a morphological reconstruction A completedescription of this method can be found in the article by PhilippeSalembler entitled “Morphological multiscale segmentation for imagecoding” which appeared in the magazine “Signal Processing” “N°” of 1994.This type of processing eliminates the objects smaller then a certainsize, and restores the contours of the objects which have not beeneliminated. At the end of this step there is therefore a simplifiedversion of the sub-band, which will be easier to process by means of thefollowing steps.

The following step E81 is an extraction of the regions or in other wordsthe contours of the segmentation S_(N−i) which was calculated at theprevious step E23, for the lower resolution level.

To extract these contours, the following procedures are for examplecarried out: all the coefficients of the resolution level processed arescanned, each coefficient different from at least one of its neighboursis considered to be a contour point, and a contour coefficient or labelcorresponding to this coefficient of the image, in an image of contoursC_(N−i), is set to a first pre-determined non-zero value (for example255); if the coefficient is similar to all its neighbours, it is notconsidered to be a contour point, and then a second pre-determinedvalue, for example, the value 0, is allocated to the contour coefficientcorresponding to it. Naturally, any other method known to the man skillin the art can be envisaged, for example a contour extraction based on acalculation of the gradients of the image (see the work “Two dimensionalsignal and image processing”, J. S. Lim, Prentice Hall International, p476).

The following step E82 effects a scaling of the image of the contoursC_(N−i) previously formed by enlargement; thus the image of the contoursat the end of this step is an image with the same resolution as thecurrent low sub-band LLS_(N−i−1) which it is sought to segment. A simpleinterpolation of the image is for example used: each supplementary pixelin the new contour image is calculated as being equal to the mean of itstwo neighbours. At the end of this step, all the pixels whose value isdifferent from the predefined value of 0 are considered to contourpoints. It should be noted that another effect of this step is“thickening” of the contours, which is particularly advantageous for thesubsequent step E84. It should also be noted that steps E81 and E82 caneasily be reversed, effecting first of all the scaling of thesegmentation, and then the extraction of the contours of this new scaledsegmentation image.

The following step E83 is the marking of or the extraction of themarkers from the simplified sub-band. Unlike step E91, this step usesnot only the simplified sub-band but also the image of the contourobtained at the previous steps This step identifies the presence of thehomogeneous regions of the simplified sub-band, using a criterion whichcan for example be a criterion of homogeneity of the intensity of theregion (flat regions); these homogeneous regions cannot cross a contourdefined by the contour image. In other words, the contours of thecontour image, and therefore the boundaries of the regions of theprevious segmentation, are considered to be impassable walls for the newsegmentation: the new segmentation is therefore forced to respect theregions established at the previous segmentation step at the lowerresolution level, and thus a continuity (coherence) of the hierarchicalsegmentation is ensured, which will be described in more detail withreference to FIG. 5. In concrete terms, use is made here for example ofa region growth algorithm: the sub-band is scanned in its entirety (forexample from top to bottom and right to left). A “Kernel” is sought,that is to say, a point, here a coefficient, representing a new region(the first coefficient of the sub-band will automatically be one ofthese). The characteristic of this region (the mean value) is calculatedon the basis of this point. Then all the neighbours of this point arethen examined, and for each of the neighbours two possibilities areoffered:

if the point encountered has an intensity close to the mean value of theregion in question, and is situated inside a region (label equal to 0 inthe contour image). It is allocated to the current region, and thestatistics of this region are updated according to this new element,

if the point encountered has an intensity which is different (in thesense of a proximity criterion) from the mean value of the region, or ifit belongs to a contour (label different from 0 in the contour image) Itis not allocated to the region (in the first case it may subsequently beconsidered to be a new “kernel” representing a new region).

All the points allocated to the current region are then themselvessubject to examination, thus to say all their neighbours are examined(growth phase).

The processing of the region continues thus until all the pointsadjacent to the points belonging to the region and not belonging to acontour have been examined At the end of this processing, the region isconsidered to be acceptable or not. If it is not acceptable (typicallytoo small) it is the decision step which will process the points of theregion in question. If it is acceptable, the processing is terminatedfor this new region. A unique label or identifier is then allocated toall the points of the region. The global processing then continues withthe search for a new kernel.

The following step E84 is the decision. It is identical to step E92 andits result is the segmentation S_(N−i−1). It consists of attaching to aregion all the points which do not have a label at the end of theassisted marking step E83 (typically the points which have been attachedto excessively small regions, and the contour points). This step can beeffected simply by considering each of the points which do not have alabel, and allocating it to the neighbouring region to which it isclosest (in the sense of a proximity criterion). The importance ofhaving generated sufficiently “thick” contours during a previous stepwill be noted here: this is because, the resolution of the imageincreasing, the precision on the contours is finer and finer, and it istherefore particularly important to allow the decision algorithm toattach all the points which are situated dose to the contour to the mostappropriate region. Thus a progressive adaptation of the location of thecontours is effected.

FIG. 5 depicts the step E83 of assisted marking of a sub-part of aresolution level and more particularly of a sub-band. This step includesthe sub-steps E100 to E112.

This algorithm identifies the presence of the homogeneous regions of thesimplified sub-band, using a criterion which can for example be acriterion of homogeneity of the intensity of the region (flat regions):these homogeneous regions cannot cross a contour defined by the image ofthe contours. In other words, the contours of the contour image, andtherefore the boundaries of the regions of the previous segmentation,are considered to be impassable walls for the new segmentation: the newsegmentation is therefore forced to respect the regions which have beenestablished at the previous segmentation step at the lower resolutionlevel, and thus continuity (coherence) of the hierarchical segmentationis ensured.

Step E100 is an initiation of scanning of the sub-band. During therunning of the algorithm, the sub-band is scanned in its entirety (forexample from top to bottom and right to left). At this step a firstcoefficient of the sub-band is considered.

At the following step E101 a new kernel is sought. For the firstpassage, the first coefficient of the sub-band is considered to be akernel.

If a kernel is found, step E101 is Followed by step E102, which is aninitalisation for creating a region to which a characteristic isallocated. The characteristic is for example the mean value of thecoefficients of the region. At this step, the characteristic is thevalue of the kernel found at the previous step,

Step E103 considers a variable referred to as the current point andassociates it with the kernel found at step E101. This current pointwill be the variable used in the loop of seeking points similar to theregion.

A neighbour of the current point is read at step E104.

The following step E105 is a comparison of the current neighbour withthe characteristics of the current region and verification in thecontour image it the label of this neighbour in the image of thecontours is at the predetermined value corresponding to a region.

In the affirmative, step E105 is followed by step E106, at which thispoint is allocated to the current region and the characteristics of theregion are recalculated taking account of this point.

If the response is negative at step E105, the neighbour is for exampleclose to the characteristics of the region but its label in the image ofthe contours is not at the predetermined value corresponding to anyregion. This means that this point formed part of a contour during thesegmentation of the previous level and is consequently rejected. StepE105 is then followed by step E108, just as step E106 is followed bystep E108.

Step E108 consists of checking whether the current neighbour is the lastneighbour. If the response is negative, step E108 is followed by thepreviously described step E104.

If the response is positive at step E108, all the neighbours of thecurrent point have been run through, and step E108 is followed by stepE109, which is a test for checking whether there remains at least onecoefficient of the region to be processed A non-processed point is aneighbour which has been retained in the region, but whose ownneighbours have not been examined.

If the response is positive at step E109, the latter is followed by stepE110, which is the reading of an unprocessed coefficient and whichallocates its value to the current point variable. Stop E110 is followedby the previously described step E104.

If the response is negative at step E109, all the region has beenscanned, and step E109 is followed by step E101 in order to seek anotherkernel.

If this search is fruitless (all the points of the sub-bands have beenexamined), step E101 is followed by step E111, which checks whether thepreviously determined regions are too small, The minimum size of theregions is generally a parameter of the segmentation algorithm. In theaffirmative, the regions concerned will be eliminated at step E112.

Steps E111 and E112 are followed by the previously described step E84.

In accordance with FIG. 6, the analysis device according to theinvention has:

means of decomposing all the data on a plurality of resolution levels,

means of first segmentation of at least one sub-part of a givenresolution level, into at least two homogeneous regions, said givenresolution level not being the highest resolution level in thedecomposition,

means of extracting contour data from the result of the segmentation ofthe previous step,

means of second segmentation of at least one sub-part of the resolutionlevel higher than the given level into at least one homogeneous region,according to the extracted contour data.

One embodiment of the device according to the invention has a signalsource 30, here for an image signal IM, whether it is a fixed image oran image sequence. In general terms, the signal source either containsthe digital signal, and has for example a memory, a hard disk or aCD-ROM, or converts an analogue signal into a digital signal, and is forexample an analogue camcorder associated with an analogue to digitalconverter. The image source 30 generates a series of digital samplesrepresenting an image IM. The image signal IM is a series of digitalwords, for example bytes. Each byte value represents a pixel of theimage IMP here with 256 levels of grey or in colour.

An output of the signal source 30 is connected to a circuit 60 fordecomposing the image IM into frequency sub-bands, as will be detailedhereinafter with reference to FIG. 7. For example, the decompositionwill be carried out on three resolution levels thus supplying sub-bandsLL₃, HL₃, LH₃ and HH₃ of the lowest resolution level RES₂, the sub-bandsHL₂, LH₂ and HH₂ of intermediate resolution RES₂, and the sub-bands HL₁,LH₂ and HH₁ of the highest resolution RES₁. It should be noted that,during this operation, the sub-bands are stored in whole or in part.

The circuit 60 is connected to a circuit 61 for segmenting the lowsub-band of the current resolution level. The circuit 61 supplies asegmentation map of the current level. The low sub-band is question isthe sub-band LL₃ during the first passage, otherwise it is a case eitherof the low sub-band synthesised by the reconstruction synthesis unit 64,or the sub-band of higher level if this was stored at the time ofdecomposition. With this segmentation effected, at least some of theinformation obtained by the segmentation is supplied to a storagecircuit 62 which stores this information.

The segmentation circuit makes it possible to extract contour data bymeans of the following operations which are performed for eachcoefficient of the segmented sub-band:

comparison of said coefficient with its neighbours,

setting a contour coefficient corresponding to said coefficient to afirst predetermined value if the coefficient is different from at leastone of its neighbours, or to a second predetermined value if thecoefficient is similar to all its neighbours.

The circuit 62 is connected to an assisted segmentation circuit 63. Theassisted segmentation circuit segments the low sub-band of highresolution level with the information previously stored by the circuit62.

The assisted segmentation circuit is adapted to perform:

a projection of a contour image resulting from the first segmentation,onto the low sub-band of the higher resolution level which is to besegmented,

a marking of the coefficients of said low sub-band of the higherresolution level, according to the result of the projection, and

a decision.

If all the sub-bands were stored at the time of decomposition, thesegmentation circuit 63 reads the low sub-band of higher resolutionstored in memory 62, otherwise the reconstruction circuit 64reconstructs, using the current sub-band and the other sub-bands ofcurrent resolution level, a low sub-band of higher resolution, which isused by the circuit 63.

The circuit 63 supplies as an output a hierarchical segmentation of theimage IM.

According to FIG. 7, the circuit 60 has three successive analysis unitsfor decomposing the image IM into sub-bands according to threeresolution levels.

In general terms, the resolution of a signal is the number of samplesper unit length used for representing this signal. In the case of animage signal, the resolution of a sub-band is related to the number ofsamples per unit length for representing this sub-band. The resolutiondepends notably on the number of decimations performed.

The first analysis unit receives the digital image signal and applies itto two digital filters, respectively low pass and high pass 601 and 602,which filter the image signal in a first direction, for examplehorizontal in the case of an image signal. After passing throughdecimators by two 6100 and 6200, the resulting filtered signals arerespectively applied to two filters, low pass 603 and 605, and high pass604 and 606, which filter them in a second direction, for examplevertical in the case of an image signal. Each resulting filtered signalpasses through a respective decimator by two 6300, 6400, 6500 and 6600.The first unit delivers as an output four sub-bands LL₁, LH₁, HL₁ andHH₁ of the highest resolution RES₁ in the decomposition.

The sub-band LL₁ includes the components, or coefficients, of lowfrequency, in both directions, of the image signal The sub-band LH₁includes the components of low frequency in the first direction and ofhigh frequency in a second direction, of the image signal. The sub-bandHL₁ includes the components of high frequency in the first direction andthe components of low frequency in the second direction. Finally thesub-band HH₁ includes the components of high frequency in bothdirections.

Each sub-band is an image constructed from the original image, whichcontains information corresponding to a respectively vertical,horizontal and diagonal orientation of the image, in a given frequencyband.

The sub-band LL₁ is analysed by an analysis unit similar to the previousone for supplying four sub-bands LL₂, LH₂, HL₂ and HH₂ of resolutionlevel RES₂ intermediate in the decomposition. The sub-band LL₂ includesthe components of low frequency in the two analysis directions, and isin its turn analysed by the third analysis unit similar to the twoprevious ones. The third analysis unit supplies sub-bands LL₃, LH₃, HL₃and HH₃, of lowest resolution level RES₃ in the decomposition, resultingfrom the division of the sub-band LL₂ into sub-bands.

Each of the sub-bands of resolution RES₂ and RES₃ also corresponds to anorientations in the image.

The decomposition effected by the circuit 60 is such that a sub-band ofa given resolution is divided into four sub-bands of lower resolutionand therefore has four times more coefficients than each of thesub-bands of lower resolution.

A digital image IM output from the image source 30 is depictedschematically in FIG. 8, whilst FIG. 9 depicts the image IMD resultingfrom the decomposition of the image IM, into ten sub-bands according tothree resolution levels, by the circuit 60. The image IMD contains asmuch information as the original image IM, but the information isdivided in frequency according to three resolution levels.

The lowest resolution level RES₃ contains the sub-bands LL₃, HL₃, LH₃and HH₃, that is to say the sub-bands of low frequency in the twoanalysis directions. The second resolution level RES₂ includes thesub-bands HL₂, LH₂ and HH₂ and the highest resolution level RES₁includes the sub-bands of higher frequency HL₁, LH₁ and HH₁.

The sub-band LL3 of lowest frequency is a reduction of the originalimage. The other sub-bands are detail sub-bands.

Naturally, the number of resolution levels, and consequently of subbands, can be chosen differently, for example 13 sub-bands and fourresolution levels, for a bi-dimensional signal such as an image. Thenumber of sub-bands per resolution level can also be different. Theanalysis and synthesis circuits are adapted to the dimension of thesignal being processed.

Naturally, the present invention is in no way limited to the embodimentsdescribed and depicted, but quite the contrary encompasses any variantwithin the capability of an expert.

What is claimed is:
 1. Method of analysing a set of data representingphysical quantities, including the steps of: decomposition (E20) of theset of data on a plurality of resolution levels, first segmentation(E21) of at least one sub-part of a given resolution level, into atleast two homogeneous regions, said given resolution level not being thehighest resolution level in the decomposition, characterised in that itincludes the steps of: extraction (E81) of contour data from the resultof the segmentation of the previous step, second segmentation (E25) ofat least one sub-part of the resolution level higher than the givenlevel into at least one homogeneous region, as a function of the contourdata extracted.
 2. Analysis method according to claim 1, characterisedin that the decomposition (E20) is at each resolution level adecomposition into a plurality of frequency sub-bands.
 3. Analysismethod according to claim 2, characterised in that the firstsegmentation (E21) is effected on a low-frequency sub-band of the givenresolution level.
 4. Analysis method according to any one of claims 1 to3, characterised in that the given resolution level is the lowestresolution level.
 5. Analysis method according to claim 4, characterisedin that the extraction and second segmentation steps (E25) are effectediteratively as far as the highest resolution level.
 6. Analysis methodaccording to claim 5, characterized in that the second segmentation stepincludes: a projection (E82) of a contour image resulting from the firstsegmentation, on said at least one sub-part of the higher resolutionlevel which is to be segmented, a marking (E83) of the coefficients ofsaid at least one sub-part of the higher resolution level, as a functionof the result of the projection, and a decision (E84).
 7. Analysismethod according to claim 6, characterised in that the secondsegmentation (E25) is effected on a low-frequency sub-band of the higherresolution level.
 8. Analysis method according to claim 7, characterisedin that the contour data extraction (E81) includes, for each coefficientof the segmented sub-part: the comparison of said coefficient with itsneighbours, setting of a contour coefficient corresponding to saidcoefficient to a first predetermined value if the coefficient isdifferent from at least one of its neighbours, or to a secondpredetermined value if the coefficient is similar to all its neighbours.9. Device for analysing a set of data representing physical quantities,having: means of decomposing (60) the set of data on a plurality ofresolution levels, means (61) for the first segmentation of at least onesub-part of a given resolution level, into at least two homogeneousregions, said given resolution level not being the highest resolutionlevel in the decomposition, characterised in that it has: means (62) ofextracting contour data from the result of the segmentation of theprevious step, means (63) for the second segmentation of at least onesub-part of the resolution level higher than the given resolution levelinto at least one homogeneous region, as a function of the extractedcontour data.
 10. Analysis device according to claim 9, characterized inthat the decomposition means (60) are adapted to effect a decompositionwhich is, at each resolution level, a decomposition into a plurality offrequency sub-bands.
 11. Analysis device according to claim 10,characterised in that the fist segmentation means (61) are adapted toeffect a segmentation on one low-frequency sub-band of the givenresolution level.
 12. Analysis device according to any one of claims 9to 11, characterised in that the first segmentation means (61) areadapted to consider a given resolution level which is the lowestresolution level.
 13. Analysis device according to claim 12,characterised in that the means of extraction (62) and secondsegmentation (63) are adapted to function iteratively as far as thehighest resolution level.
 14. Analysis device according to claim 13,characterised in that the second segmentation means (63) are adapted toeffect: a projection of a contour image resulting from the firstsegmentation, onto said at least one sub-part of the higher resolutionlevel which is to be segmented, a marking of the coefficients of said atleast one sub-part of the higher resolution level, as a function of theresult of the projection, and a decision.
 15. Analysis device accordingto claim 14, characterised in that the second segmentation means (63)are adapted to effect a second segmentation on a low-frequency sub-bandof the higher resolution level.
 16. Analysis device according to claim15, characterised in that the contour data extraction means (61, 62) areadapted to effect the following operations, for each coefficient of thesegmented sub-part: comparison of said coefficient with its neighbours,setting a contour coefficient corresponding to said coefficient to afirst predetermined value if the coefficient is different from at leastone of its neighbours, or to a second predetermined value if thecoefficient is similar to all its neighbours.
 17. Analysis device (10)according to claim 16, characterised in that the decomposition,extraction and first and second segmentation means are incorporated in:a microprocessor (100), a read only memory (102) containing a programfor processing the data, and a random access memory (103) containingregisters adapted to record variable modified during the running of saidprogram.
 18. Digital signal processing apparatus, characterised in thatit has means adapted to implement the method according to any one ofclaims 1 to
 3. 19. Digital signal processing apparatus, characterised inthat it includes the device according to any one of claims 9 to
 11. 20.Storage medium storing a program for implementing a method according toany one of claims 1 to
 3. 21. Storage medium according to claim 20,characterised in that said storage medium is a floppy disk or a CD-ROM.22. A storage medium detachably mountable on a device according to anyone of claims 9 to 11, wherein said storage medium stores a program forimplementing a method of analyzing a set of data representing physicalquantities, including the steps of: decomposing the set data on aplurality of resolution levels; segmenting at least one sub-part of agiven resolution level, into at least two homogeneous regions, the givenresolution level not being the highest resolution level in thedecomposition; extracting contour data from the result of thesegmentation of said segmenting step; and segmenting at least onesub-part of the resolution level higher than the given level into atleast one homogeneous region, as a function of the contour dataextracted.
 23. The storage medium according to claim 22, wherein saidstorage medium is a floppy disk or a CD-ROM.